Prerequisite: None
Across the data management industry, the term “data intelligence” is used broadly for data catalogs, metadata management in data platforms, semantic layers, master data management (MDM), content and search taxonomies, and more, creating confusion. However, most definitions share the critical concept that in an era of fast data creation, collection, and streaming volume into the petabytes, with highly varied data and content coming from thousands of sources landing in multiple cloud platforms and on-premises systems, organizations need knowledge about their data and content to gain value.
Without good data intelligence, organizations cannot ensure adherence to data governance policies and regulations as they build data pipelines, data products, AI models, and data-rich operational applications. Without knowledge about data lineage, data quality problems are nearly impossible to track back to sources. Users cannot find the best data sets for their projects. Delays, errors, and lack of trust in data products and AI models prevent organizations from reaching data-driven goals.
David Stodder will discuss current trends and directions in data intelligence for meeting needs driven by AI, democratized BI and analytics, and governance requirements. Key topics will include:
- Data intelligence and metadata management trends with modern data platforms, including for Apache Iceberg
- Directions toward holistic data intelligence above silos for distributed data fabric, governance, and data mesh architecture
- Trends in semantic layers, semantic data integration, and use of knowledge graphs for data intelligence
- Modern master data management (MDM) and how it fits into data intelligence